The role of eGovernment and geographic information technology is crucial in the resolution of environmental problems such as Amazon deforestation. Yet, the assessment of deforestation based on Geographic Information Systems is highly dependent on the availability of accurate geospatial information. In this study, a Random Forest modeling approach is developed using open data and free software to identify spatially explicit deforestation drivers and areas vulnerable to forest transitions in the department of San Martín, Peru. The results of this study show that it was possible to explain 88% of the variance in deforestation based on open geospatial data, and to generate a deforestation vulnerability map with an overall classification accuracy of 89%. The modeling approach proposed in this study could aid local governments in remote tropical areas, coping with limited data availability and financial resources, to examine deforestation trends and target environmental conservation policies.